Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations2154048
Missing cells14380032
Missing cells (%)35.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory851.5 MiB
Average record size in memory414.5 B

Variable types

Text2
Categorical3
Numeric13
Boolean1

Alerts

MRG has constant value "False"Constant
ARPU_SEGMENT is highly overall correlated with FREQUENCE and 7 other fieldsHigh correlation
CHURN is highly overall correlated with REGULARITYHigh correlation
FREQUENCE is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
FREQUENCE_RECH is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
FREQ_TOP_PACK is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
MONTANT is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
ON_NET is highly overall correlated with ARPU_SEGMENT and 4 other fieldsHigh correlation
ORANGE is highly overall correlated with ARPU_SEGMENT and 6 other fieldsHigh correlation
REGULARITY is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
REVENUE is highly overall correlated with ARPU_SEGMENT and 7 other fieldsHigh correlation
TENURE is highly imbalanced (86.4%)Imbalance
REGION has 849299 (39.4%) missing valuesMissing
MONTANT has 756739 (35.1%) missing valuesMissing
FREQUENCE_RECH has 756739 (35.1%) missing valuesMissing
REVENUE has 726048 (33.7%) missing valuesMissing
ARPU_SEGMENT has 726048 (33.7%) missing valuesMissing
FREQUENCE has 726048 (33.7%) missing valuesMissing
DATA_VOLUME has 1060433 (49.2%) missing valuesMissing
ON_NET has 786675 (36.5%) missing valuesMissing
ORANGE has 895248 (41.6%) missing valuesMissing
TIGO has 1290016 (59.9%) missing valuesMissing
ZONE1 has 1984327 (92.1%) missing valuesMissing
ZONE2 has 2017224 (93.6%) missing valuesMissing
TOP_PACK has 902594 (41.9%) missing valuesMissing
FREQ_TOP_PACK has 902594 (41.9%) missing valuesMissing
DATA_VOLUME is highly skewed (γ1 = 36.25674263)Skewed
ZONE1 is highly skewed (γ1 = 25.70889323)Skewed
ZONE2 is highly skewed (γ1 = 30.88518917)Skewed
user_id has unique valuesUnique
DATA_VOLUME has 320153 (14.9%) zerosZeros
ON_NET has 108046 (5.0%) zerosZeros
ORANGE has 61623 (2.9%) zerosZeros
TIGO has 94270 (4.4%) zerosZeros
ZONE1 has 59935 (2.8%) zerosZeros
ZONE2 has 40440 (1.9%) zerosZeros

Reproduction

Analysis started2025-10-14 11:07:00.268387
Analysis finished2025-10-14 11:10:15.018421
Duration3 minutes and 14.75 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

user_id
Text

Unique 

Distinct2154048
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size182.8 MiB
2025-10-14T14:10:17.377520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length40
Median length40
Mean length40
Min length40

Characters and Unicode

Total characters86161920
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2154048 ?
Unique (%)100.0%

Sample

1st row00000bfd7d50f01092811bc0c8d7b0d6fe7c3596
2nd row00000cb4a5d760de88fecb38e2f71b7bec52e834
3rd row00001654a9d9f96303d9969d0a4a851714a4bb57
4th row00001dd6fa45f7ba044bd5d84937be464ce78ac2
5th row000028d9e13a595abe061f9b58f3d76ab907850f
ValueCountFrequency (%)
ffff56138e6bf8e553514dfb97ee7cbe0f6cc6091
 
< 0.1%
ffff5956b3770fca0c1f5a1c907d74ff603e8ff91
 
< 0.1%
ffff6410f18958f9558a229475cfc54bc2b158ef1
 
< 0.1%
ffff6e41acb8a069e888c4e8fbd9779f1e0bde731
 
< 0.1%
ffff8da611b1f7591fae91245f93a6dcf276056a1
 
< 0.1%
ffffa921a44c8611cf12a5a0277c4238d4a637491
 
< 0.1%
ffffb2b8b63959b8a374e2a2ccaf2b9e521879ad1
 
< 0.1%
ffffc38e1c3cb77a88941e739c358fd96bce32381
 
< 0.1%
ffffccdae4d9097c20f95e87f5c89845cab4eff31
 
< 0.1%
ffffd1d48dd02c059c82c70b8793c8dfa3d095931
 
< 0.1%
Other values (2154038)2154038
> 99.9%
2025-10-14T14:10:19.503665image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
25393160
 
6.3%
b5389543
 
6.3%
c5387121
 
6.3%
05387072
 
6.3%
95385666
 
6.3%
d5385205
 
6.3%
a5384709
 
6.2%
55384389
 
6.2%
85384268
 
6.2%
45383987
 
6.2%
Other values (6)32296800
37.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)86161920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25393160
 
6.3%
b5389543
 
6.3%
c5387121
 
6.3%
05387072
 
6.3%
95385666
 
6.3%
d5385205
 
6.3%
a5384709
 
6.2%
55384389
 
6.2%
85384268
 
6.2%
45383987
 
6.2%
Other values (6)32296800
37.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)86161920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25393160
 
6.3%
b5389543
 
6.3%
c5387121
 
6.3%
05387072
 
6.3%
95385666
 
6.3%
d5385205
 
6.3%
a5384709
 
6.2%
55384389
 
6.2%
85384268
 
6.2%
45383987
 
6.2%
Other values (6)32296800
37.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)86161920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25393160
 
6.3%
b5389543
 
6.3%
c5387121
 
6.3%
05387072
 
6.3%
95385666
 
6.3%
d5385205
 
6.3%
a5384709
 
6.2%
55384389
 
6.2%
85384268
 
6.2%
45383987
 
6.2%
Other values (6)32296800
37.5%

REGION
Categorical

Missing 

Distinct14
Distinct (%)< 0.1%
Missing849299
Missing (%)39.4%
Memory size114.2 MiB
DAKAR
513271 
THIES
180052 
SAINT-LOUIS
119886 
LOUGA
99053 
KAOLACK
96986 
Other values (9)
295501 

Length

Max length11
Median length5
Mean length6.3267073
Min length5

Characters and Unicode

Total characters8254765
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFATICK
2nd rowDAKAR
3rd rowDAKAR
4th rowLOUGA
5th rowLOUGA

Common Values

ValueCountFrequency (%)
DAKAR513271
23.8%
THIES180052
 
8.4%
SAINT-LOUIS119886
 
5.6%
LOUGA99053
 
4.6%
KAOLACK96986
 
4.5%
DIOURBEL66911
 
3.1%
TAMBACOUNDA55074
 
2.6%
KAFFRINE43963
 
2.0%
KOLDA38743
 
1.8%
FATICK35643
 
1.7%
Other values (4)55167
 
2.6%
(Missing)849299
39.4%

Length

2025-10-14T14:10:19.639218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
dakar513271
39.3%
thies180052
 
13.8%
saint-louis119886
 
9.2%
louga99053
 
7.6%
kaolack96986
 
7.4%
diourbel66911
 
5.1%
tambacounda55074
 
4.2%
kaffrine43963
 
3.4%
kolda38743
 
3.0%
fatick35643
 
2.7%
Other values (4)55167
 
4.2%

Most occurring characters

ValueCountFrequency (%)
A1781190
21.6%
K826612
10.0%
D678138
 
8.2%
R646090
 
7.8%
I613350
 
7.4%
O503757
 
6.1%
S422943
 
5.1%
L421579
 
5.1%
T419738
 
5.1%
U368028
 
4.5%
Other values (10)1573340
19.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)8254765
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
A1781190
21.6%
K826612
10.0%
D678138
 
8.2%
R646090
 
7.8%
I613350
 
7.4%
O503757
 
6.1%
S422943
 
5.1%
L421579
 
5.1%
T419738
 
5.1%
U368028
 
4.5%
Other values (10)1573340
19.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)8254765
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
A1781190
21.6%
K826612
10.0%
D678138
 
8.2%
R646090
 
7.8%
I613350
 
7.4%
O503757
 
6.1%
S422943
 
5.1%
L421579
 
5.1%
T419738
 
5.1%
U368028
 
4.5%
Other values (10)1573340
19.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)8254765
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
A1781190
21.6%
K826612
10.0%
D678138
 
8.2%
R646090
 
7.8%
I613350
 
7.4%
O503757
 
6.1%
S422943
 
5.1%
L421579
 
5.1%
T419738
 
5.1%
U368028
 
4.5%
Other values (10)1573340
19.1%

TENURE
Categorical

Imbalance 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.4 MiB
K > 24 month
2043201 
I 18-21 month
 
45278
H 15-18 month
 
26006
G 12-15 month
 
14901
J 21-24 month
 
12725
Other values (3)
 
11937

Length

Max length13
Median length12
Mean length12.044707
Min length11

Characters and Unicode

Total characters25944877
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowK > 24 month
2nd rowI 18-21 month
3rd rowK > 24 month
4th rowK > 24 month
5th rowK > 24 month

Common Values

ValueCountFrequency (%)
K > 24 month2043201
94.9%
I 18-21 month45278
 
2.1%
H 15-18 month26006
 
1.2%
G 12-15 month14901
 
0.7%
J 21-24 month12725
 
0.6%
F 9-12 month9328
 
0.4%
E 6-9 month1839
 
0.1%
D 3-6 month770
 
< 0.1%

Length

2025-10-14T14:10:19.776380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T14:10:19.934655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
month2154048
25.3%
k2043201
24.0%
2043201
24.0%
242043201
24.0%
i45278
 
0.5%
18-2145278
 
0.5%
h26006
 
0.3%
15-1826006
 
0.3%
g14901
 
0.2%
12-1514901
 
0.2%
Other values (8)49324
 
0.6%

Most occurring characters

ValueCountFrequency (%)
6351297
24.5%
n2154048
 
8.3%
o2154048
 
8.3%
m2154048
 
8.3%
h2154048
 
8.3%
t2154048
 
8.3%
22138158
 
8.2%
42055926
 
7.9%
K2043201
 
7.9%
>2043201
 
7.9%
Other values (14)542854
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)25944877
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
6351297
24.5%
n2154048
 
8.3%
o2154048
 
8.3%
m2154048
 
8.3%
h2154048
 
8.3%
t2154048
 
8.3%
22138158
 
8.2%
42055926
 
7.9%
K2043201
 
7.9%
>2043201
 
7.9%
Other values (14)542854
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)25944877
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
6351297
24.5%
n2154048
 
8.3%
o2154048
 
8.3%
m2154048
 
8.3%
h2154048
 
8.3%
t2154048
 
8.3%
22138158
 
8.2%
42055926
 
7.9%
K2043201
 
7.9%
>2043201
 
7.9%
Other values (14)542854
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)25944877
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
6351297
24.5%
n2154048
 
8.3%
o2154048
 
8.3%
m2154048
 
8.3%
h2154048
 
8.3%
t2154048
 
8.3%
22138158
 
8.2%
42055926
 
7.9%
K2043201
 
7.9%
>2043201
 
7.9%
Other values (14)542854
 
2.1%

MONTANT
Real number (ℝ)

High correlation  Missing 

Distinct6540
Distinct (%)0.5%
Missing756739
Missing (%)35.1%
Infinite0
Infinite (%)0.0%
Mean5532.117
Minimum10
Maximum470000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:20.121519image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile250
Q11000
median3000
Q37350
95-th percentile18500
Maximum470000
Range469990
Interquartile range (IQR)6350

Descriptive statistics

Standard deviation7111.3394
Coefficient of variation (CV)1.2854644
Kurtosis57.528484
Mean5532.117
Median Absolute Deviation (MAD)2400
Skewness4.2297262
Sum7.7300769 × 109
Variance50571148
MonotonicityNot monotonic
2025-10-14T14:10:20.279887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500112976
 
5.2%
100082997
 
3.9%
150048710
 
2.3%
200046122
 
2.1%
20040004
 
1.9%
300034831
 
1.6%
250032026
 
1.5%
400024109
 
1.1%
350023793
 
1.1%
10020188
 
0.9%
Other values (6530)931553
43.2%
(Missing)756739
35.1%
ValueCountFrequency (%)
103
 
< 0.1%
202
 
< 0.1%
221
 
< 0.1%
252
 
< 0.1%
302
 
< 0.1%
351
 
< 0.1%
371
 
< 0.1%
401
 
< 0.1%
481
 
< 0.1%
50360
< 0.1%
ValueCountFrequency (%)
4700001
< 0.1%
2905001
< 0.1%
2865001
< 0.1%
2650001
< 0.1%
2595001
< 0.1%
2560001
< 0.1%
2355001
< 0.1%
2350001
< 0.1%
2310002
< 0.1%
2306001
< 0.1%

FREQUENCE_RECH
Real number (ℝ)

High correlation  Missing 

Distinct123
Distinct (%)< 0.1%
Missing756739
Missing (%)35.1%
Infinite0
Infinite (%)0.0%
Mean11.52912
Minimum1
Maximum133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:20.438584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median7
Q316
95-th percentile40
Maximum133
Range132
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.27407
Coefficient of variation (CV)1.1513515
Kurtosis5.3169562
Mean11.52912
Median Absolute Deviation (MAD)5
Skewness2.1119879
Sum16109743
Variance176.20092
MonotonicityNot monotonic
2025-10-14T14:10:20.619613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1219471
 
10.2%
2139897
 
6.5%
3110506
 
5.1%
488889
 
4.1%
574527
 
3.5%
664115
 
3.0%
755616
 
2.6%
849983
 
2.3%
944715
 
2.1%
1040655
 
1.9%
Other values (113)508935
23.6%
(Missing)756739
35.1%
ValueCountFrequency (%)
1219471
10.2%
2139897
6.5%
3110506
5.1%
488889
4.1%
574527
 
3.5%
664115
 
3.0%
755616
 
2.6%
849983
 
2.3%
944715
 
2.1%
1040655
 
1.9%
ValueCountFrequency (%)
1331
 
< 0.1%
1321
 
< 0.1%
1311
 
< 0.1%
1221
 
< 0.1%
1211
 
< 0.1%
1191
 
< 0.1%
1181
 
< 0.1%
1172
< 0.1%
1154
< 0.1%
1142
< 0.1%

REVENUE
Real number (ℝ)

High correlation  Missing 

Distinct38114
Distinct (%)2.7%
Missing726048
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean5510.8103
Minimum1
Maximum532177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:20.779058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile199
Q11000
median3000
Q37368
95-th percentile18791
Maximum532177
Range532176
Interquartile range (IQR)6368

Descriptive statistics

Standard deviation7187.1129
Coefficient of variation (CV)1.3041844
Kurtosis64.821825
Mean5510.8103
Median Absolute Deviation (MAD)2498
Skewness4.1890021
Sum7.8694372 × 109
Variance51654592
MonotonicityNot monotonic
2025-10-14T14:10:20.962967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50058783
 
2.7%
100036269
 
1.7%
150020740
 
1.0%
20020043
 
0.9%
200018220
 
0.8%
300013211
 
0.6%
250012096
 
0.6%
35008727
 
0.4%
40008303
 
0.4%
1007893
 
0.4%
Other values (38104)1223715
56.8%
(Missing)726048
33.7%
ValueCountFrequency (%)
14295
0.2%
23134
0.1%
3211
 
< 0.1%
41961
0.1%
5104
 
< 0.1%
61111
 
0.1%
7522
 
< 0.1%
81225
 
0.1%
91230
 
0.1%
102691
0.1%
ValueCountFrequency (%)
5321771
< 0.1%
3979681
< 0.1%
3235411
< 0.1%
2721911
< 0.1%
2660501
< 0.1%
2440011
< 0.1%
2400941
< 0.1%
2335831
< 0.1%
2334131
< 0.1%
2331411
< 0.1%

ARPU_SEGMENT
Real number (ℝ)

High correlation  Missing 

Distinct16535
Distinct (%)1.2%
Missing726048
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean1836.9429
Minimum0
Maximum177392
Zeros4295
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:21.128372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile66
Q1333
median1000
Q32456
95-th percentile6264
Maximum177392
Range177392
Interquartile range (IQR)2123

Descriptive statistics

Standard deviation2395.7
Coefficient of variation (CV)1.3041777
Kurtosis64.822078
Mean1836.9429
Median Absolute Deviation (MAD)833
Skewness4.1890192
Sum2.6231545 × 109
Variance5739378.3
MonotonicityNot monotonic
2025-10-14T14:10:21.301054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16767878
 
3.2%
33343705
 
2.0%
50028568
 
1.3%
66722898
 
1.1%
6722753
 
1.1%
100018483
 
0.9%
83315341
 
0.7%
116711524
 
0.5%
133310725
 
0.5%
3310473
 
0.5%
Other values (16525)1175652
54.6%
(Missing)726048
33.7%
ValueCountFrequency (%)
04295
0.2%
15306
0.2%
21737
 
0.1%
35146
0.2%
42755
0.1%
51819
 
0.1%
61175
 
0.1%
73439
0.2%
8728
 
< 0.1%
91090
 
0.1%
ValueCountFrequency (%)
1773921
< 0.1%
1326561
< 0.1%
1078471
< 0.1%
907301
< 0.1%
886831
< 0.1%
813341
< 0.1%
800311
< 0.1%
778611
< 0.1%
778041
< 0.1%
777141
< 0.1%

FREQUENCE
Real number (ℝ)

High correlation  Missing 

Distinct91
Distinct (%)< 0.1%
Missing726048
Missing (%)33.7%
Infinite0
Infinite (%)0.0%
Mean13.978141
Minimum1
Maximum91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:21.466414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median9
Q320
95-th percentile45
Maximum91
Range90
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.694035
Coefficient of variation (CV)1.0512152
Kurtosis3.402515
Mean13.978141
Median Absolute Deviation (MAD)7
Skewness1.7750807
Sum19960786
Variance215.91466
MonotonicityNot monotonic
2025-10-14T14:10:21.622721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1161585
 
7.5%
2116460
 
5.4%
395237
 
4.4%
482338
 
3.8%
571867
 
3.3%
664228
 
3.0%
757343
 
2.7%
851893
 
2.4%
947532
 
2.2%
1043694
 
2.0%
Other values (81)635823
29.5%
(Missing)726048
33.7%
ValueCountFrequency (%)
1161585
7.5%
2116460
5.4%
395237
4.4%
482338
3.8%
571867
3.3%
664228
 
3.0%
757343
 
2.7%
851893
 
2.4%
947532
 
2.2%
1043694
 
2.0%
ValueCountFrequency (%)
9183
 
< 0.1%
9084
 
< 0.1%
89126
 
< 0.1%
88165
 
< 0.1%
87214
< 0.1%
86291
< 0.1%
85268
< 0.1%
84322
< 0.1%
83362
< 0.1%
82481
< 0.1%

DATA_VOLUME
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct41550
Distinct (%)3.8%
Missing1060433
Missing (%)49.2%
Infinite0
Infinite (%)0.0%
Mean3366.4502
Minimum0
Maximum1823866
Zeros320153
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:21.782491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median257
Q32895
95-th percentile14981
Maximum1823866
Range1823866
Interquartile range (IQR)2895

Descriptive statistics

Standard deviation13304.464
Coefficient of variation (CV)3.952075
Kurtosis2448.1241
Mean3366.4502
Median Absolute Deviation (MAD)257
Skewness36.256743
Sum3.6816004 × 109
Variance1.7700875 × 108
MonotonicityNot monotonic
2025-10-14T14:10:21.963295image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0320153
 
14.9%
141366
 
1.9%
213233
 
0.6%
37326
 
0.3%
45613
 
0.3%
10245469
 
0.3%
54678
 
0.2%
10233794
 
0.2%
63778
 
0.2%
73202
 
0.1%
Other values (41540)685003
31.8%
(Missing)1060433
49.2%
ValueCountFrequency (%)
0320153
14.9%
141366
 
1.9%
213233
 
0.6%
37326
 
0.3%
45613
 
0.3%
54678
 
0.2%
63778
 
0.2%
73202
 
0.1%
82920
 
0.1%
92837
 
0.1%
ValueCountFrequency (%)
18238661
< 0.1%
17023091
< 0.1%
15568291
< 0.1%
13523041
< 0.1%
13268751
< 0.1%
12974641
< 0.1%
12727201
< 0.1%
12389151
< 0.1%
11548091
< 0.1%
11177351
< 0.1%

ON_NET
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct9884
Distinct (%)0.7%
Missing786675
Missing (%)36.5%
Infinite0
Infinite (%)0.0%
Mean277.68914
Minimum0
Maximum50809
Zeros108046
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:22.126920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median27
Q3156
95-th percentile1358
Maximum50809
Range50809
Interquartile range (IQR)151

Descriptive statistics

Standard deviation872.68891
Coefficient of variation (CV)3.1426829
Kurtosis116.85712
Mean277.68914
Median Absolute Deviation (MAD)26
Skewness8.1479278
Sum3.7970463 × 108
Variance761585.93
MonotonicityNot monotonic
2025-10-14T14:10:22.292319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0108046
 
5.0%
192118
 
4.3%
258773
 
2.7%
342296
 
2.0%
741382
 
1.9%
438699
 
1.8%
838501
 
1.8%
529845
 
1.4%
629496
 
1.4%
919640
 
0.9%
Other values (9874)868577
40.3%
(Missing)786675
36.5%
ValueCountFrequency (%)
0108046
5.0%
192118
4.3%
258773
2.7%
342296
 
2.0%
438699
 
1.8%
529845
 
1.4%
629496
 
1.4%
741382
 
1.9%
838501
 
1.8%
919640
 
0.9%
ValueCountFrequency (%)
508091
< 0.1%
450111
< 0.1%
386481
< 0.1%
366871
< 0.1%
341051
< 0.1%
334521
< 0.1%
321411
< 0.1%
317681
< 0.1%
304251
< 0.1%
298611
< 0.1%

ORANGE
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct3167
Distinct (%)0.3%
Missing895248
Missing (%)41.6%
Infinite0
Infinite (%)0.0%
Mean95.418711
Minimum0
Maximum21323
Zeros61623
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:22.453634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q17
median29
Q399
95-th percentile392
Maximum21323
Range21323
Interquartile range (IQR)92

Descriptive statistics

Standard deviation204.98727
Coefficient of variation (CV)2.1482921
Kurtosis189.03871
Mean95.418711
Median Absolute Deviation (MAD)27
Skewness8.0540159
Sum1.2011307 × 108
Variance42019.779
MonotonicityNot monotonic
2025-10-14T14:10:22.621853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
168881
 
3.2%
061623
 
2.9%
249435
 
2.3%
336198
 
1.7%
433933
 
1.6%
825826
 
1.2%
524649
 
1.1%
622373
 
1.0%
721218
 
1.0%
1020250
 
0.9%
Other values (3157)894414
41.5%
(Missing)895248
41.6%
ValueCountFrequency (%)
061623
2.9%
168881
3.2%
249435
2.3%
336198
1.7%
433933
1.6%
524649
 
1.1%
622373
 
1.0%
721218
 
1.0%
825826
 
1.2%
919954
 
0.9%
ValueCountFrequency (%)
213231
< 0.1%
120401
< 0.1%
76601
< 0.1%
73141
< 0.1%
67881
< 0.1%
67211
< 0.1%
65551
< 0.1%
64291
< 0.1%
64161
< 0.1%
63191
< 0.1%

TIGO
Real number (ℝ)

Missing  Zeros 

Distinct1315
Distinct (%)0.2%
Missing1290016
Missing (%)59.9%
Infinite0
Infinite (%)0.0%
Mean23.109253
Minimum0
Maximum4174
Zeros94270
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:22.784342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q320
95-th percentile95
Maximum4174
Range4174
Interquartile range (IQR)18

Descriptive statistics

Standard deviation63.578086
Coefficient of variation (CV)2.7511961
Kurtosis334.67472
Mean23.109253
Median Absolute Deviation (MAD)5
Skewness12.899932
Sum19967134
Variance4042.173
MonotonicityNot monotonic
2025-10-14T14:10:22.938625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1112165
 
5.2%
094270
 
4.4%
272530
 
3.4%
353003
 
2.5%
442980
 
2.0%
534524
 
1.6%
629421
 
1.4%
726170
 
1.2%
824304
 
1.1%
920835
 
1.0%
Other values (1305)353830
 
16.4%
(Missing)1290016
59.9%
ValueCountFrequency (%)
094270
4.4%
1112165
5.2%
272530
3.4%
353003
2.5%
442980
 
2.0%
534524
 
1.6%
629421
 
1.4%
726170
 
1.2%
824304
 
1.1%
920835
 
1.0%
ValueCountFrequency (%)
41741
< 0.1%
38001
< 0.1%
37281
< 0.1%
37061
< 0.1%
36581
< 0.1%
34861
< 0.1%
29551
< 0.1%
28991
< 0.1%
28601
< 0.1%
27961
< 0.1%

ZONE1
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct612
Distinct (%)0.4%
Missing1984327
Missing (%)92.1%
Infinite0
Infinite (%)0.0%
Mean8.1701322
Minimum0
Maximum4792
Zeros59935
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:23.091833image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile32
Maximum4792
Range4792
Interquartile range (IQR)3

Descriptive statistics

Standard deviation41.169511
Coefficient of variation (CV)5.0390264
Kurtosis1572.6889
Mean8.1701322
Median Absolute Deviation (MAD)1
Skewness25.708893
Sum1386643
Variance1694.9287
MonotonicityNot monotonic
2025-10-14T14:10:23.260428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
059935
 
2.8%
141376
 
1.9%
216858
 
0.8%
39264
 
0.4%
46044
 
0.3%
54434
 
0.2%
63233
 
0.2%
72573
 
0.1%
82134
 
0.1%
92060
 
0.1%
Other values (602)21810
 
1.0%
(Missing)1984327
92.1%
ValueCountFrequency (%)
059935
2.8%
141376
1.9%
216858
 
0.8%
39264
 
0.4%
46044
 
0.3%
54434
 
0.2%
63233
 
0.2%
72573
 
0.1%
82134
 
0.1%
92060
 
0.1%
ValueCountFrequency (%)
47921
< 0.1%
25071
< 0.1%
19861
< 0.1%
18671
< 0.1%
18391
< 0.1%
18041
< 0.1%
17301
< 0.1%
16841
< 0.1%
16591
< 0.1%
16571
< 0.1%

ZONE2
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct486
Distinct (%)0.4%
Missing2017224
Missing (%)93.6%
Infinite0
Infinite (%)0.0%
Mean7.5533094
Minimum0
Maximum3697
Zeros40440
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:23.412040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q35
95-th percentile29
Maximum3697
Range3697
Interquartile range (IQR)5

Descriptive statistics

Standard deviation33.487234
Coefficient of variation (CV)4.4334519
Kurtosis2107.0549
Mean7.5533094
Median Absolute Deviation (MAD)2
Skewness30.885189
Sum1033474
Variance1121.3948
MonotonicityNot monotonic
2025-10-14T14:10:23.576620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
040440
 
1.9%
126941
 
1.3%
215428
 
0.7%
39857
 
0.5%
47393
 
0.3%
54836
 
0.2%
63723
 
0.2%
73231
 
0.1%
82360
 
0.1%
92074
 
0.1%
Other values (476)20541
 
1.0%
(Missing)2017224
93.6%
ValueCountFrequency (%)
040440
1.9%
126941
1.3%
215428
 
0.7%
39857
 
0.5%
47393
 
0.3%
54836
 
0.2%
63723
 
0.2%
73231
 
0.1%
82360
 
0.1%
92074
 
0.1%
ValueCountFrequency (%)
36971
< 0.1%
31431
< 0.1%
20081
< 0.1%
17961
< 0.1%
16181
< 0.1%
13511
< 0.1%
13461
< 0.1%
13241
< 0.1%
13211
< 0.1%
13161
< 0.1%

MRG
Boolean

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size2.1 MiB
False
2154048 
ValueCountFrequency (%)
False2154048
100.0%
2025-10-14T14:10:23.679439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

REGULARITY
Real number (ℝ)

High correlation 

Distinct62
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.042505
Minimum1
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:23.779835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median24
Q351
95-th percentile62
Maximum62
Range61
Interquartile range (IQR)45

Descriptive statistics

Standard deviation22.286857
Coefficient of variation (CV)0.79475271
Kurtosis-1.4871698
Mean28.042505
Median Absolute Deviation (MAD)20
Skewness0.24740754
Sum60404902
Variance496.70399
MonotonicityNot monotonic
2025-10-14T14:10:23.943778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1195162
 
9.1%
62166477
 
7.7%
2118915
 
5.5%
386027
 
4.0%
468335
 
3.2%
6164431
 
3.0%
556823
 
2.6%
649771
 
2.3%
6047515
 
2.2%
744483
 
2.1%
Other values (52)1256109
58.3%
ValueCountFrequency (%)
1195162
9.1%
2118915
5.5%
386027
4.0%
468335
 
3.2%
556823
 
2.6%
649771
 
2.3%
744483
 
2.1%
841208
 
1.9%
937397
 
1.7%
1034883
 
1.6%
ValueCountFrequency (%)
62166477
7.7%
6164431
 
3.0%
6047515
 
2.2%
5939821
 
1.8%
5834710
 
1.6%
5731831
 
1.5%
5629166
 
1.4%
5527491
 
1.3%
5426417
 
1.2%
5325147
 
1.2%

TOP_PACK
Text

Missing 

Distinct140
Distinct (%)< 0.1%
Missing902594
Missing (%)41.9%
Memory size113.7 MiB
2025-10-14T14:10:24.174753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length49
Median length42
Mean length23.185356
Min length7

Characters and Unicode

Total characters29015406
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)< 0.1%

Sample

1st rowOn net 200F=Unlimited _call24H
2nd rowOn-net 1000F=10MilF;10d
3rd rowData:1000F=5GB,7d
4th rowMixt 250F=Unlimited_call24H
5th rowMIXT:500F= 2500F on net _2500F off net;2d
ValueCountFrequency (%)
all-net387647
 
12.4%
500f=2000f;5d317802
 
10.2%
net257671
 
8.3%
on238197
 
7.6%
200f=unlimited152295
 
4.9%
call24h152295
 
4.9%
2500f128824
 
4.1%
data127980
 
4.1%
data:490f=1gb,7d115180
 
3.7%
mixt91930
 
2.9%
Other values (186)1152864
36.9%
2025-10-14T14:10:24.533312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
04097436
 
14.1%
1871231
 
6.4%
l1758228
 
6.1%
F1688162
 
5.8%
t1615813
 
5.6%
n1458987
 
5.0%
21358477
 
4.7%
e1172573
 
4.0%
a1113680
 
3.8%
51106572
 
3.8%
Other values (61)11774247
40.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)29015406
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
04097436
 
14.1%
1871231
 
6.4%
l1758228
 
6.1%
F1688162
 
5.8%
t1615813
 
5.6%
n1458987
 
5.0%
21358477
 
4.7%
e1172573
 
4.0%
a1113680
 
3.8%
51106572
 
3.8%
Other values (61)11774247
40.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29015406
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
04097436
 
14.1%
1871231
 
6.4%
l1758228
 
6.1%
F1688162
 
5.8%
t1615813
 
5.6%
n1458987
 
5.0%
21358477
 
4.7%
e1172573
 
4.0%
a1113680
 
3.8%
51106572
 
3.8%
Other values (61)11774247
40.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29015406
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
04097436
 
14.1%
1871231
 
6.4%
l1758228
 
6.1%
F1688162
 
5.8%
t1615813
 
5.6%
n1458987
 
5.0%
21358477
 
4.7%
e1172573
 
4.0%
a1113680
 
3.8%
51106572
 
3.8%
Other values (61)11774247
40.6%

FREQ_TOP_PACK
Real number (ℝ)

High correlation  Missing 

Distinct245
Distinct (%)< 0.1%
Missing902594
Missing (%)41.9%
Infinite0
Infinite (%)0.0%
Mean9.2724615
Minimum1
Maximum713
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.4 MiB
2025-10-14T14:10:24.669554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q312
95-th percentile33
Maximum713
Range712
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.280443
Coefficient of variation (CV)1.3243995
Kurtosis61.726468
Mean9.2724615
Median Absolute Deviation (MAD)4
Skewness4.1120661
Sum11604059
Variance150.80928
MonotonicityNot monotonic
2025-10-14T14:10:24.827517image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1251882
 
11.7%
2155396
 
7.2%
3116447
 
5.4%
485552
 
4.0%
568531
 
3.2%
657092
 
2.7%
749478
 
2.3%
843188
 
2.0%
938731
 
1.8%
1034641
 
1.6%
Other values (235)350516
 
16.3%
(Missing)902594
41.9%
ValueCountFrequency (%)
1251882
11.7%
2155396
7.2%
3116447
5.4%
485552
 
4.0%
568531
 
3.2%
657092
 
2.7%
749478
 
2.3%
843188
 
2.0%
938731
 
1.8%
1034641
 
1.6%
ValueCountFrequency (%)
7131
< 0.1%
6291
< 0.1%
6241
< 0.1%
6121
< 0.1%
5921
< 0.1%
5601
< 0.1%
5441
< 0.1%
5111
< 0.1%
4521
< 0.1%
4331
< 0.1%

CHURN
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size102.7 MiB
0
1750062 
1
403986 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2154048
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
01750062
81.2%
1403986
 
18.8%

Length

2025-10-14T14:10:24.977205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-14T14:10:25.054265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
01750062
81.2%
1403986
 
18.8%

Most occurring characters

ValueCountFrequency (%)
01750062
81.2%
1403986
 
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)2154048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
01750062
81.2%
1403986
 
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)2154048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
01750062
81.2%
1403986
 
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)2154048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
01750062
81.2%
1403986
 
18.8%

Interactions

2025-10-14T14:09:47.345431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:52.433213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:57.490837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:02.630794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:07.674472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:12.998504image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:18.191841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:22.577453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:27.269273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:32.146411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:35.784381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:38.209931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:41.059591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:47.974499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:52.897405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:57.949668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:03.067211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:08.119747image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:13.496009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:18.541697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:22.986602image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:27.666279image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:32.457361image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:35.970801image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:38.432441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:41.773748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:48.576961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:53.351912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:58.398251image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:03.539872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:08.571211image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:13.953245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:18.906904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:23.394414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:28.083591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:32.792011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:36.170592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:38.631398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:42.352358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:49.315057image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:53.802315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:58.838442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:03.972447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:09.002635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:14.518276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:19.266510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:23.813910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:28.551564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:33.119195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:36.358460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:38.818010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:42.970699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:49.881920image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:54.241176image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:59.327811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:04.439612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:09.445188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:15.001124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:19.640821image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:24.222598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:29.153345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:33.442843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:36.575327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:39.014529image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:43.584302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:50.280180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:54.580146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:59.666854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:04.779865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:09.795917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:15.359108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:20.023976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:24.551068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:29.506421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:33.694566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:36.763293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:39.194816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:44.049296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:50.722777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:54.988809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:00.110618image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:05.214883image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:10.280465image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:15.817351image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:20.386396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:24.970480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:29.919888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:33.997074image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:36.954235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:39.374558image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:44.549231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:51.148195image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:55.394067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:00.554520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:05.633178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:10.736993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:16.242274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:20.714987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:25.362796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:30.302403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:34.312161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:37.135013image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:39.601304image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:45.015469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:51.517560image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:55.746643image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:00.901561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:05.967521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:11.105435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:16.577088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:21.006763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:25.670415image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:30.634619image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:34.613360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:37.319447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:39.784170image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:45.379307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:51.734018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:55.928234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:01.102370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:06.152343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:11.321840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:16.778028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:21.190526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:25.860252image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:30.820086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:34.772381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:37.489049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:39.950684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:45.580675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:51.928537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:56.122414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:01.286509image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:06.333788image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:11.532911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:16.971577image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:21.382862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:26.030344image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:30.997715image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:34.931052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:37.635837image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:40.138329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:45.782987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:52.413546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:56.580616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:01.751900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:06.796569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:12.009985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:17.447535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:21.785812image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:26.474214image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:31.415037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:35.261787image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:37.829809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:40.330266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:46.390307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:52.875850image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:08:57.014385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:02.174262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:07.222907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:12.504756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:17.853817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:22.136017image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:26.853308image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:31.800754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:35.583694image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:38.022322image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:40.511255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-14T14:09:46.859871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-14T14:10:25.140371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ARPU_SEGMENTCHURNDATA_VOLUMEFREQUENCEFREQUENCE_RECHFREQ_TOP_PACKMONTANTON_NETORANGEREGIONREGULARITYREVENUETENURETIGOZONE1ZONE2
ARPU_SEGMENT1.0000.0080.3890.8800.8790.8170.9870.5190.6790.0090.7161.0000.0050.4530.2190.311
CHURN0.0081.0000.0010.1480.1080.0110.0090.0170.0070.0340.5570.0080.0500.0070.0050.009
DATA_VOLUME0.3890.0011.0000.3310.2960.2290.379-0.098-0.0210.0040.3020.3890.017-0.014-0.022-0.000
FREQUENCE0.8800.1480.3311.0000.9510.8670.8710.4380.5280.0530.6910.8800.0050.3350.0830.194
FREQUENCE_RECH0.8790.1080.2960.9511.0000.8940.8870.4760.5620.0460.6780.8790.0040.3620.0880.185
FREQ_TOP_PACK0.8170.0110.2290.8670.8941.0000.8120.4360.5360.0100.5970.8170.0000.3500.0980.065
MONTANT0.9870.0090.3790.8710.8870.8121.0000.5090.6690.0110.7070.9870.0060.4490.2150.309
ON_NET0.5190.017-0.0980.4380.4760.4360.5091.0000.5510.0080.5230.5190.0000.3680.065-0.023
ORANGE0.6790.007-0.0210.5280.5620.5360.6690.5511.0000.0080.4570.6780.0150.4710.1250.049
REGION0.0090.0340.0040.0530.0460.0100.0110.0080.0081.0000.0360.0090.0230.0070.0050.000
REGULARITY0.7160.5570.3020.6910.6780.5970.7070.5230.4570.0361.0000.7160.0160.3230.0540.043
REVENUE1.0000.0080.3890.8800.8790.8170.9870.5190.6780.0090.7161.0000.0050.4530.2190.311
TENURE0.0050.0500.0170.0050.0040.0000.0060.0000.0150.0230.0160.0051.0000.0000.0040.009
TIGO0.4530.007-0.0140.3350.3620.3500.4490.3680.4710.0070.3230.4530.0001.0000.0770.021
ZONE10.2190.005-0.0220.0830.0880.0980.2150.0650.1250.0050.0540.2190.0040.0771.0000.107
ZONE20.3110.009-0.0000.1940.1850.0650.309-0.0230.0490.0000.0430.3110.0090.0210.1071.000

Missing values

2025-10-14T14:09:53.878718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-14T14:09:57.659637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-10-14T14:10:11.376064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
000000bfd7d50f01092811bc0c8d7b0d6fe7c3596FATICKK > 24 month4250.015.04251.01417.017.04.0388.046.01.01.02.0NO54On net 200F=Unlimited _call24H8.00
100000cb4a5d760de88fecb38e2f71b7bec52e834NaNI 18-21 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO4NaNNaN1
200001654a9d9f96303d9969d0a4a851714a4bb57NaNK > 24 month3600.02.01020.0340.02.0NaN90.046.07.0NaNNaNNO17On-net 1000F=10MilF;10d1.00
300001dd6fa45f7ba044bd5d84937be464ce78ac2DAKARK > 24 month13500.015.013502.04501.018.043804.041.0102.02.0NaNNaNNO62Data:1000F=5GB,7d11.00
4000028d9e13a595abe061f9b58f3d76ab907850fDAKARK > 24 month1000.01.0985.0328.01.0NaN39.024.0NaNNaNNaNNO11Mixt 250F=Unlimited_call24H2.00
50000296564272665ccd2925d377e124f3306b01eLOUGAK > 24 month8500.017.09000.03000.018.0NaN252.070.091.0NaNNaNNO62MIXT:500F= 2500F on net _2500F off net;2d18.00
600002b0ed56e2c199ec8c3021327229afa70f063LOUGAK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO2NaNNaN0
70000313946b6849745963442c6e572d47cd24cedDAKARK > 24 month7000.016.07229.02410.022.01601.077.029.0100.0NaNNaNNO55All-net 500F=2000F;5d8.00
80000398021ccd3a488fa1a63dee3b2f0d471f9fdDAKARK > 24 month1500.03.01502.0501.012.0NaN2.053.02.0NaNNaNNO31NaNNaN0
900003d165737109921ebd21f883cb8cff028b626TAMBACOUNDAK > 24 month4000.08.04000.01333.08.0NaN1620.09.0NaNNaNNaNNO45On-net 500F_FNF;3d8.00
user_idREGIONTENUREMONTANTFREQUENCE_RECHREVENUEARPU_SEGMENTFREQUENCEDATA_VOLUMEON_NETORANGETIGOZONE1ZONE2MRGREGULARITYTOP_PACKFREQ_TOP_PACKCHURN
2154038ffffb2b8b63959b8a374e2a2ccaf2b9e521879adNaNK > 24 month1000.02.01000.0333.02.00.02.012.03.0NaNNaNNO12All-net 500F=2000F;5d2.00
2154039ffffc38e1c3cb77a88941e739c358fd96bce3238DAKARK > 24 monthNaNNaNNaNNaNNaNNaNNaN25.0NaNNaNNaNNO6NaNNaN0
2154040ffffccdae4d9097c20f95e87f5c89845cab4eff3SAINT-LOUISK > 24 month2000.04.01997.0666.05.00.057.01.0NaN2.0NaNNO21All-net 500F=2000F;5d2.00
2154041ffffd1d48dd02c059c82c70b8793c8dfa3d09593NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN0
2154042ffffd3057e31ff19496a3c00397a9a67d5037c52DAKARK > 24 month4800.04.04800.01600.014.07400.02.012.0NaNNaN0.0NO62Data:1000F=2GB,30d3.00
2154043ffffe85215ddc71a84f95af0afb0deeea90e6967NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO6NaNNaN0
2154044ffffeaaa9289cdba0ac000f0ab4b48f4aa74ed15THIESK > 24 month6100.015.05800.01933.015.0621.026.040.040.0NaNNaNNO55Data: 200 F=100MB,24H9.00
2154045fffff172fda1b4bb38a95385951908bb92379809NaNK > 24 monthNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNO1NaNNaN1
2154046fffff5911296937a37f09a37a549da2e0dad6dbbTHIESK > 24 month10000.011.07120.02373.013.0NaN0.0140.013.0NaNNaNNO28All-net 500F=2000F;5d12.00
2154047fffff6dbff1508ea2bfe814e5ab2729ce6b788c2NaNK > 24 monthNaNNaNNaNNaNNaN2.0NaNNaNNaNNaNNaNNO62NaNNaN1